package cn.task.config;

import cn.task.store.MongoChatMemoryStore;
import dev.langchain4j.data.document.Document;
import dev.langchain4j.data.document.loader.FileSystemDocumentLoader;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.memory.chat.ChatMemoryProvider;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.rag.content.retriever.ContentRetriever;
import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
import dev.langchain4j.service.MemoryId;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

import java.util.Arrays;
import java.util.Collections;
import java.util.List;

/**
 @author Mengru Jiao
 @date 2025/5/29
 @project java-ai-langchain4j
 */
@Configuration
public class XiaozhiAgentConfig {
    @Autowired
    private MongoChatMemoryStore mongoChatMemoryStore;
    @Autowired
    private EmbeddingStore embeddingStore;
    @Autowired
    private EmbeddingModel embeddingModel;
    @Bean
    public ChatMemoryProvider chatMemoryProviderXiaozhi(){
        return memoryId -> MessageWindowChatMemory.builder()
                .id(memoryId).
                maxMessages(20).chatMemoryStore(mongoChatMemoryStore).build();
    }
    // 内容检索库的配置
    /*@Bean
    public ContentRetriever contentRetrieverXiaozhi(){
        Document document1 = FileSystemDocumentLoader.loadDocument("E:/knowledge/医院信息.md");
        Document document2 = FileSystemDocumentLoader.loadDocument("E:/knowledge/科室信息.md");
        Document document3 = FileSystemDocumentLoader.loadDocument("E:/knowledge/神经内科.md");
        List<Document> documents = Arrays.asList(document1, document2, document3);
        //定义使用内存向量存储
        InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
        //使用默认的文档分割器
        EmbeddingStoreIngestor.ingest(documents, embeddingStore);
        //从嵌入存储（EmbeddingStore）里检索和查询内容相关的信息
        return EmbeddingStoreContentRetriever.from(embeddingStore);
    }*/
    @Bean
    public ContentRetriever contentRetrieverXiaozhi() {
         return EmbeddingStoreContentRetriever.builder()
                 .embeddingModel(embeddingModel).embeddingStore(embeddingStore).maxResults(1)
                 .build();
    }
}
